1,732 research outputs found
Interaction Methods for Smart Glasses : A Survey
Since the launch of Google Glass in 2014, smart glasses have mainly been designed to support micro-interactions. The ultimate goal for them to become an augmented reality interface has not yet been attained due to an encumbrance of controls. Augmented reality involves superimposing interactive computer graphics images onto physical objects in the real world. This survey reviews current research issues in the area of human-computer interaction for smart glasses. The survey first studies the smart glasses available in the market and afterwards investigates the interaction methods proposed in the wide body of literature. The interaction methods can be classified into hand-held, touch, and touchless input. This paper mainly focuses on the touch and touchless input. Touch input can be further divided into on-device and on-body, while touchless input can be classified into hands-free and freehand. Next, we summarize the existing research efforts and trends, in which touch and touchless input are evaluated by a total of eight interaction goals. Finally, we discuss several key design challenges and the possibility of multi-modal input for smart glasses.Peer reviewe
NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models
Objective. Spike sorting, a critical step in neural data processing, aims to
classify spiking events from single electrode recordings based on different
waveforms. This study aims to develop a novel online spike sorter, NeuSort,
using neuromorphic models, with the ability to adaptively adjust to changes in
neural signals, including waveform deformations and the appearance of new
neurons. Approach. NeuSort leverages a neuromorphic model to emulate
template-matching processes. This model incorporates plasticity learning
mechanisms inspired by biological neural systems, facilitating real-time
adjustments to online parameters. Results. Experimental findings demonstrate
NeuSort's ability to track neuron activities amidst waveform deformations and
identify new neurons in real-time. NeuSort excels in handling non-stationary
neural signals, significantly enhancing its applicability for long-term spike
sorting tasks. Moreover, its implementation on neuromorphic chips guarantees
ultra-low energy consumption during computation. Significance. NeuSort caters
to the demand for real-time spike sorting in brain-machine interfaces through a
neuromorphic approach. Its unsupervised, automated spike sorting process makes
it a plug-and-play solution for online spike sorting
Press-n-Paste : Copy-and-Paste Operations with Pressure-sensitive Caret Navigation for Miniaturized Surface in Mobile Augmented Reality
Publisher Copyright: © 2021 ACM.Copy-and-paste operations are the most popular features on computing devices such as desktop computers, smartphones and tablets. However, the copy-and-paste operations are not sufficiently addressed on the Augmented Reality (AR) smartglasses designated for real-time interaction with texts in physical environments. This paper proposes two system solutions, namely Granularity Scrolling (GS) and Two Ends (TE), for the copy-and-paste operations on AR smartglasses. By leveraging a thumb-size button on a touch-sensitive and pressure-sensitive surface, both the multi-step solutions can capture the target texts through indirect manipulation and subsequently enables the copy-and-paste operations. Based on the system solutions, we implemented an experimental prototype named Press-n-Paste (PnP). After the eight-session evaluation capturing 1,296 copy-and-paste operations, 18 participants with GS and TE achieve the peak performance of 17,574 ms and 13,951 ms per copy-and-paste operation, with 93.21% and 98.15% accuracy rates respectively, which are as good as the commercial solutions using direct manipulation on touchscreen devices. The user footprints also show that PnP has a distinctive feature of miniaturized interaction area within 12.65 mm∗14.48 mm. PnP not only proves the feasibility of copy-and-paste operations with the flexibility of various granularities on AR smartglasses, but also gives significant implications to the design space of pressure widgets as well as the input design on smart wearables.Peer reviewe
Development and Characterization of Supercooled Polyethylene Naphthalate
The utilization of undercooled or supercooled polymers presents a promising approach for the creation of single-polymer composites (SPCs), applicable not only to compaction processing but also to extrusion, injection molding, and 3D printing techniques. This study focuses on the development and characterization of supercooled polyethylene naphthalate (PEN) through differential scanning calorimetry (DSC) and rheological measurements. By employing predetermined conditions, a supercooling degree of 50 ËšC for PEN was achieved. The impact of maximum heating temperature, cooling rate, and shear rate on the supercooling degree was examined, revealing that higher supercooling degrees of PEN can be attained by increasing these factors. Additionally, the flow behavior of supercooled polymer melts at various temperatures was analyzed. The supercooling state of PEN exhibited remarkable stability for a minimum duration of half an hour at temperatures exceeding 250 ËšC
Learning Physically Realizable Skills for Online Packing of General 3D Shapes
We study the problem of learning online packing skills for irregular 3D
shapes, which is arguably the most challenging setting of bin packing problems.
The goal is to consecutively move a sequence of 3D objects with arbitrary
shapes into a designated container with only partial observations of the object
sequence. Meanwhile, we take physical realizability into account, involving
physics dynamics and constraints of a placement. The packing policy should
understand the 3D geometry of the object to be packed and make effective
decisions to accommodate it in the container in a physically realizable way. We
propose a Reinforcement Learning (RL) pipeline to learn the policy. The complex
irregular geometry and imperfect object placement together lead to huge
solution space. Direct training in such space is prohibitively data intensive.
We instead propose a theoretically-provable method for candidate action
generation to reduce the action space of RL and the learning burden. A
parameterized policy is then learned to select the best placement from the
candidates. Equipped with an efficient method of asynchronous RL acceleration
and a data preparation process of simulation-ready training sequences, a mature
packing policy can be trained in a physics-based environment within 48 hours.
Through extensive evaluation on a variety of real-life shape datasets and
comparisons with state-of-the-art baselines, we demonstrate that our method
outperforms the best-performing baseline on all datasets by at least 12.8% in
terms of packing utility.Comment: ACM Transactions on Graphics (TOG
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